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bioRxiv · 10.1101/2020.09.05.284539

Characterizing tissue composition through combined analysis of single-cell morphologies and transcriptional states

Bao, F.·Deng, Y.·Wan, S.·Wang, B.·Dai, Q.·Altschuler, S.·Wu, L.
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Abstract

Advances in spatial transcriptomics technologies enable optical profiling of morphological and transcriptional modalities from the same cells within tissues. Here, we present multi-modal structured embedding (MUSE), an approach to deeply characterize tissue heterogeneity through analysis of combined image and transcriptional single-cell measurements. We demonstrate that MUSE can discover cellular subpopulations missed by either modality as well as compensate for modality-specific noise. MUSE identified biologically meaningful cellular subpopulations and stereotyped spatial patterning within heterogeneous mouse cortex brain tissues, profiled by seqFISH+ or STARmap technologies. MUSE provides a framework for combining multi-modal single-cell data to reveal deeper insights into the states, functions and organization of cells in complex biological tissues.

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Bao, F., Deng, Y., Wan, S., Wang, B., Dai, Q., Altschuler, S., Wu, L.. 2020-09-05. Characterizing tissue composition through combined analysis of single-cell morphologies and transcriptional states. https://doi.org/10.1101/2020.09.05.284539

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